While content analysis exists in both quantitative and qualitative research, we are going to focus on qualitative content analysis, sometimes called QCA. Sometimes content analysis is applied as a blanket term for looking to see what qualitative data is about (ie the content) and from there, many approaches can be applied. Hsieh and Shannon (2005) describe the main analytical focus as being “characteristics of language as communication with attention to the content or contextual meaning of the text” – very different to a narrative focus, but similar to some types of discourse analysis. However, it almost always takes a approach to identifying and coding to concepts, topics or models - a ‘reductive’ approach where concepts can be clearly defined, and quantified or counted in some way.
Concepts (possibly also called themes or codes) are created during an ‘abstraction’ process (Elo and Kyngäs 2008), where the researcher is responsible for identifying a set of concepts that describe important things in the data. Content analysis for Elo and Kyngäs can be inductive (theory building) or deductive (testing existing theory).
While many forms of content analysis do take a very basic counting approach to examining words and discourse in the data, the classic textbook Basic Content Analysis (Weber 1990) also discusses the importance of understanding the “sender(s) of the message, the message itself, or the audience of the message”. In this understanding, the context of the data is as important as the content itself. This is where the lines of definition are blurred between many other types of qualitative analysis, such as discourse analysis and IPA.
However, qualitative content analysis generally gets described with fairly positivistic language:
“A research method for the subjective interpretation of the content of text data through the systematic classification process of coding and identifying themes or patterns” (Hsieh and Shannon 2005)
“An approach of empirical, methodological controlled analysis of texts within their context of communication, following content analytic rules and step by step models, without rash quantification” (Mayring, 2000)
Not that it can’t be combined with more ‘fluffy’ approaches like grounded theory, but it does tend to be described more in fields and disciplines with quantitative and mixed method approaches. Similarly, Drisko and Maschi (2016) describe an emphasis on transparency, validity and replicability – all terms that are not aims of other approaches that take a more feminist epistimology, like grounded theory and thematic analysis.
It’s another one of those terms (like grounded theory, or thematic analysis) that is actually a overarching term for some very different approaches. Hsieh and Shannon (2005) describe three distinct approaches within content analysis: conventional, directed, or summative. For them,
“In conventional content analysis, coding categories are derived directly from the text data. With a directed approach, analysis starts with a theory or relevant research findings as guidance for initial codes. A summative content analysis involves counting and comparisons, usually of keywords or content, followed by the interpretation of the underlying context.”
The first two seem to map on well to the inductive and deductive approaches defined by Elo and Kyngäs (2008), while the summative approach is closer to the more quantitative form of content analysis, similar to keyword analysis.
So what is the main difference with qualitative content analysis? I would argue that most of the differences are semantic or epistemological, but it is likely that the focus on counting, set steps, and systematic procedures and processes are important distinguishing features. Now, many other types of qualitative analysis also have prescribed stages (like Open and Axial coding in grounded theory), and authors have described many different processes for applying qualitative content analysis – for example Schreler (2012) focuses on coding frames (kind of similar to codebooks). Thus it’s difficult to describe one over-arching process, you just need to look at a couple of authors, and see which processes will best fit your data and epistemology.
However, let’s look at some of the basic commonalities, and how you would go about operationally doing content analysis in Quirkos and other qualitative research software.
Using the WordCloud and word count feature, it is easy to do basic (summative) content analysis and even limited quantitative content analysis. You can visualise, and get numeric counts for commonly occurring words in your text sources, but also filter so you only see results from certain sources. However, this is quite a limited approach for rich qualitative data, and generally you’d also want to look at pairing this with either of the two other approaches: conventional/inductive or directed/deductive.
The inductive approach is quite a similar theme generation approach to grounded theory, and with a similar aim: you are trying to develop new theory, but letting the data suggest themes as you go through and read the data. Thus you don’t start with a list of likely themes or codes, but create new ones as they go along, probably following an iterative or cyclical process of also grouping codes into themes in later stages. Quirkos makes it easy to create codes as you go along, just drag and drop text onto the (+) button to create a new code or theme. You can also group them, and create hierarchies of codes into themes by dragging and dropping them on to each other.
For the deductive/directed approach, themes or topics are created from the literature and existing research (probably as part of a literature review). This is why it can be helpful to bring your literature into Quirkos or other qualitative analysis software and do your literature review there. Create codes and themes representing the major topics and theories in the literature, then later bring in your data and code it to the same themes. That way it’s easy to see how your data compares to the literature, something you’ll almost have to do when writing up.
So the basic tools of qualitative software like Quirkos can work well for any of the different sub-types of qualitative content analysis, and really you need to read some of these different interpretations in full to find the best fit for you. All the references are below to help you do this, and you can also try Quirkos for free to see if the visual and intuitive interface is a good fit for your qualitative analysis journey!
Elo, S., Kyngäs, H., 2008, The qualitative content analysis process, Journal of Advanced Nursing, 62(1), https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1365-2648.2007.04569.x
Hsieh, H.-F., & Shannon, S.E. (2005). Three approaches to qualitative content analysis.
Qualitative Health Research, 15(9), 1277-1288. https://study.sagepub.com/sites/default/files/Hsieh%20and%20Shannon%20-%20Three%20Approaches%20to%20Qualitative%20Content%20Analysis.pdf
Mayring, P. (2000). Qualitative content analysis. Forum: Qualitative Social Research, 1(2). https://www.qualitative-research.net/index.php/fqs/article/view/1089/2386
Mayring P. (2015) Qualitative Content Analysis: Theoretical Background and Procedures. In: Bikner-Ahsbahs A., Knipping C., Presmeg N. (eds) Approaches to Qualitative Research in Mathematics Education. Advances in Mathematics Education. Springer. https://doi.org/10.1007/978-94-017-9181-6_13
Schreler, M., 2012, Qualitative Content Analysis in Practice, Sage.
Weber, R. P. (1990). Basic content analysis (2nd ed.). Sage. Google Scholar